
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__snake_case				      =    """▁"""

__snake_case				      =    {"""vocab_file""": """spiece.model"""}

__snake_case				      =    {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}

__snake_case				      =    {
    """google/pegasus-xsum""": 5_12,
}


__snake_case				      =    logging.get_logger(__name__)




class 		lowercase__    (		_UpperCAmelCase    ):
	A__       :  Optional[Any]										=VOCAB_FILES_NAMES

	A__       :  int										=VOCAB_FILES_NAMES
	A__       :  List[str]										=PRETRAINED_VOCAB_FILES_MAP
	A__       :  List[str]										=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A__       :  Tuple										=['''input_ids''', '''attention_mask''']
	def __init__(     self				:  Optional[int]			,  UpperCAmelCase_				:  str			,  UpperCAmelCase_				:  Union[str, Any]="<pad>"			,  UpperCAmelCase_				:  List[str]="</s>"			,  UpperCAmelCase_				:  Optional[Any]="<unk>"			,  UpperCAmelCase_				:  int="<mask_2>"			,  UpperCAmelCase_				:  Union[str, Any]="<mask_1>"			,  UpperCAmelCase_				:  Union[str, Any]=None			,  UpperCAmelCase_				:  Union[str, Any]=103			,  UpperCAmelCase_				:  Optional[Dict[str, Any]] = None			,  **UpperCAmelCase_				:  Tuple			,  ):
						SCREAMING_SNAKE_CASE__						=					offset
						if additional_special_tokens is not None:
											if not isinstance(UpperCAmelCase_			,  UpperCAmelCase_				):
																raise TypeError(
																    F'additional_special_tokens should be of type {type(UpperCAmelCase_				)}, but is'
																    F' {type(UpperCAmelCase_				)}'				)

											SCREAMING_SNAKE_CASE__						=					(
											    ([mask_token_sent] + additional_special_tokens)
											    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
											    else additional_special_tokens
											)
											# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
											additional_special_tokens_extended += [
											    F'<unk_{i}>' for i in range(len(UpperCAmelCase_				)			,  self.offset - 1				)
											]

											if len(set(UpperCAmelCase_				)				) != len(UpperCAmelCase_				):
																raise ValueError(
																    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
																    F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'				)
											SCREAMING_SNAKE_CASE__						=					additional_special_tokens_extended
						else:
											SCREAMING_SNAKE_CASE__						=					[mask_token_sent] if mask_token_sent is not None else []
											additional_special_tokens += [F'<unk_{i}>' for i in range(2			,  self.offset				)]

						SCREAMING_SNAKE_CASE__						=					{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    eos_token=UpperCAmelCase_			,  unk_token=UpperCAmelCase_			,  mask_token=UpperCAmelCase_			,  pad_token=UpperCAmelCase_			,  mask_token_sent=UpperCAmelCase_			,  offset=UpperCAmelCase_			,  additional_special_tokens=UpperCAmelCase_			,  sp_model_kwargs=self.sp_model_kwargs			,  **UpperCAmelCase_			,  )
						SCREAMING_SNAKE_CASE__						=					mask_token_sent
						SCREAMING_SNAKE_CASE__						=					vocab_file
						SCREAMING_SNAKE_CASE__						=					spm.SentencePieceProcessor(**self.sp_model_kwargs				)
						self.sp_model.Load(UpperCAmelCase_				)

						# add special tokens to encoder dict
						SCREAMING_SNAKE_CASE__						=					{
						    0: self.pad_token,
						    1: self.eos_token,
						}

						if self.mask_token_sent is not None:
											self.encoder.update(
											    {
											        2: self.mask_token_sent,
											        3: self.mask_token,
											    }				)

						if self.offset > 0:
											# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
											# mask_token_sent is already added to list -> so start at 1
											self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1			,  self.offset - 1				)}				)

						SCREAMING_SNAKE_CASE__						=					{v: k for k, v in self.encoder.items()}
	@property
	def 	A_							(     self				:  List[Any]				):
						return len(self.sp_model				) + self.offset
	def 	A_							(     self				:  Optional[int]				):
						SCREAMING_SNAKE_CASE__						=					{self.convert_ids_to_tokens(UpperCAmelCase_				): i for i in range(self.vocab_size				)}
						vocab.update(self.added_tokens_encoder				)
						return vocab
	def __getstate__(     self				:  Any				):
						SCREAMING_SNAKE_CASE__						=					self.__dict__.copy()
						SCREAMING_SNAKE_CASE__						=					None
						return state
	def __setstate__(     self				:  Dict			,  UpperCAmelCase_				:  int				):
						SCREAMING_SNAKE_CASE__						=					d

						# for backward compatibility
						if not hasattr(self			,  'sp_model_kwargs'				):
											SCREAMING_SNAKE_CASE__						=					{}

						SCREAMING_SNAKE_CASE__						=					spm.SentencePieceProcessor(**self.sp_model_kwargs				)
						self.sp_model.Load(self.vocab_file				)
	def 	A_							(     self				:  Dict			,  UpperCAmelCase_				:  str				):
						return self.sp_model.encode(UpperCAmelCase_			,  out_type=UpperCAmelCase_				)
	def 	A_							(     self				:  List[Any]			,  UpperCAmelCase_				:  str				):


						if token in self.decoder:
											return self.decoder[token]
						elif token in self.added_tokens_decoder:
											return self.added_tokens_decoder[token]
						SCREAMING_SNAKE_CASE__						=					self.sp_model.piece_to_id(UpperCAmelCase_				)
						return sp_id + self.offset
	def 	A_							(     self				:  Dict			,  UpperCAmelCase_				:  int				):


						if index in self.encoder:
											return self.encoder[index]
						elif index in self.added_tokens_encoder:
											return self.added_tokens_encoder[index]
						else:
											SCREAMING_SNAKE_CASE__						=					self.sp_model.IdToPiece(index - self.offset				)
						return token
	def 	A_							(     self				:  Tuple			,  UpperCAmelCase_				:  Optional[Any]				):
						SCREAMING_SNAKE_CASE__						=					[]
						SCREAMING_SNAKE_CASE__						=					''
						for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
																out_string += self.sp_model.decode(UpperCAmelCase_				) + token
																SCREAMING_SNAKE_CASE__						=					[]
											else:
																current_sub_tokens.append(UpperCAmelCase_				)
						out_string += self.sp_model.decode(UpperCAmelCase_				)
						return out_string.strip()
	def 	A_							(     self				:  str			,  UpperCAmelCase_				:  str=False				):
						return 1
	def 	A_							(     self				:  Any			,  UpperCAmelCase_				:  Any				):
						SCREAMING_SNAKE_CASE__						=					set(self.all_special_ids				)  # call it once instead of inside list comp
						all_special_ids.remove(self.unk_token_id				)  # <unk> is only sometimes special

						return [1 if x in all_special_ids else 0 for x in seq]
	def 	A_							(     self				:  str			,  UpperCAmelCase_				:  List			,  UpperCAmelCase_				:  Optional[List] = None			,  UpperCAmelCase_				:  bool = False				):


						if already_has_special_tokens:
											return self._special_token_mask(UpperCAmelCase_				)
						elif token_ids_a is None:
											return self._special_token_mask(UpperCAmelCase_				) + [1]
						else:
											return self._special_token_mask(token_ids_a + token_ids_a				) + [1]
	def 	A_							(     self				:  int			,  UpperCAmelCase_				:  int			,  UpperCAmelCase_				:  int=None				):


						if token_ids_a is None:
											return token_ids_a + [self.eos_token_id]
						# We don't expect to process pairs, but leave the pair logic for API consistency
						return token_ids_a + token_ids_a + [self.eos_token_id]





	def 	A_							(     self				:  Union[str, Any]			,  UpperCAmelCase_				:  str			,  UpperCAmelCase_				:  Optional[str] = None				):


						if not os.path.isdir(UpperCAmelCase_				):
											logger.error(F'Vocabulary path ({save_directory}) should be a directory'				)
											return
						SCREAMING_SNAKE_CASE__						=					os.path.join(
						    UpperCAmelCase_			,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

						if os.path.abspath(self.vocab_file				) != os.path.abspath(UpperCAmelCase_				) and os.path.isfile(self.vocab_file				):
											copyfile(self.vocab_file			,  UpperCAmelCase_				)
						elif not os.path.isfile(self.vocab_file				):
											with open(UpperCAmelCase_			,  'wb'				) as fi:
																SCREAMING_SNAKE_CASE__						=					self.sp_model.serialized_model_proto()
																fi.write(UpperCAmelCase_				)

						return (out_vocab_file,)











'''simple docstring'''







import argparse
import copy







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     {}

   with open(UpperCAmelCase						) as f:
      for line in f:
         if line.split()[0] not in dict_of_neighbours:
            A__       =     []
            _list.append([line.split()[1], line.split()[2]]						)
            A__       =     _list
         else:
            dict_of_neighbours[line.split()[0]].append(
                [line.split()[1], line.split()[2]]						)
         if line.split()[1] not in dict_of_neighbours:
            A__       =     []
            _list.append([line.split()[0], line.split()[2]]						)
            A__       =     _list
         else:
            dict_of_neighbours[line.split()[1]].append(
                [line.split()[0], line.split()[2]]						)

   return dict_of_neighbours







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase						) as f:
      A__       =     f.read(1						)
   A__       =     start_node

   A__       =     []

   A__       =     start_node

   A__       =     0
   while visiting not in first_solution:
      A__       =     10000
      for k in dict_of_neighbours[visiting]:
         if int(k[1]						) < int(UpperCAmelCase						) and k[0] not in first_solution:
            A__       =     k[1]
            A__       =     k[0]

      first_solution.append(UpperCAmelCase						)
      A__       =     distance_of_first_solution + int(UpperCAmelCase						)
      A__       =     best_node

   first_solution.append(UpperCAmelCase						)

   A__       =     0
   for k in dict_of_neighbours[first_solution[-2]]:
      if k[0] == start_node:
         break
      position += 1

   A__       =     (
       distance_of_first_solution
       + int(dict_of_neighbours[first_solution[-2]][position][1]						)
       - 10000
   )
   return first_solution, distance_of_first_solution







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     []

   for n in solution[1:-1]:
      A__       =     solution.index(UpperCAmelCase						)
      for kn in solution[1:-1]:
         A__       =     solution.index(UpperCAmelCase						)
         if n == kn:
            continue

         A__       =     copy.deepcopy(UpperCAmelCase						)
         A__       =     kn
         A__       =     n

         A__       =     0

         for k in _tmp[:-1]:
            A__       =     _tmp[_tmp.index(UpperCAmelCase						) + 1]
            for i in dict_of_neighbours[k]:
               if i[0] == next_node:
                  A__       =     distance + int(i[1]						)
         _tmp.append(UpperCAmelCase						)

         if _tmp not in neighborhood_of_solution:
            neighborhood_of_solution.append(_tmp						)

   A__       =     len(neighborhood_of_solution[0]						) - 1

   neighborhood_of_solution.sort(key=lambda UpperCAmelCase						: x[index_of_last_item_in_the_list]						)
   return neighborhood_of_solution







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     1
   A__       =     first_solution
   A__       =     []
   A__       =     distance_of_first_solution
   A__       =     solution

   while count <= iters:
      A__       =     find_neighborhood(UpperCAmelCase      ,UpperCAmelCase						)
      A__       =     0
      A__       =     neighborhood[index_of_best_solution]
      A__       =     len(UpperCAmelCase						) - 1

      A__       =     False
      while not found:
         A__       =     0
         while i < len(UpperCAmelCase						):
            if best_solution[i] != solution[i]:
               A__       =     best_solution[i]
               A__       =     solution[i]
               break
            A__       =     i + 1

         if [first_exchange_node, second_exchange_node] not in tabu_list and [
             second_exchange_node,
             first_exchange_node,
         ] not in tabu_list:
            tabu_list.append([first_exchange_node, second_exchange_node]						)
            A__       =     True
            A__       =     best_solution[:-1]
            A__       =     neighborhood[index_of_best_solution][best_cost_index]
            if cost < best_cost:
               A__       =     cost
               A__       =     solution
         else:
            A__       =     index_of_best_solution + 1
            A__       =     neighborhood[index_of_best_solution]

      if len(UpperCAmelCase						) >= size:
         tabu_list.pop(0						)

      A__       =     count + 1

   return best_solution_ever, best_cost







def 						_A      ( UpperCAmelCase=None						):






   '''simple docstring'''







   A__       =     generate_neighbours(args.File						)

   A__  ,			A__       =     generate_first_solution(
       args.File      ,UpperCAmelCase						)

   A__  ,			A__       =     tabu_search(
       UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,args.Iterations      ,args.Size      ,)

   print(F"""Best solution: {best_sol}, with total distance: {best_cost}."""						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser(description='''Tabu Search''')
    parser.add_argument(
        '''-f''',
        '''--File''',
        type=str,
        help='''Path to the file containing the data''',
        required=True,
    )
    parser.add_argument(
        '''-i''',
        '''--Iterations''',
        type=int,
        help='''How many iterations the algorithm should perform''',
        required=True,
    )
    parser.add_argument(
        '''-s''', '''--Size''', type=int, help='''Size of the tabu list''', required=True
    )

    # Pass the arguments to main method
    main(parser.parse_args())


